import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data=pd.read_csv('Billionaires Statistics Dataset.csv')
data.head()

C:\Users\Anjal\anaconda3\lib\site-packages\scipy\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.26.2
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"

	rank	finalWorth	category	personName	age	country	city	source	industries	countryOfCitizenship	...	cpi_change_country	gdp_country	gross_tertiary_education_enrollment	gross_primary_education_enrollment_country	life_expectancy_country	tax_revenue_country_country	total_tax_rate_country	population_country	latitude_country	longitude_country
0	1	211000	Fashion & Retail	Bernard Arnault & family	74.0	France	Paris	LVMH	Fashion & Retail	France	...	1.1	$2,715,518,274,227	65.6	102.5	82.5	24.2	60.7	67059887.0	46.227638	2.213749
1	2	180000	Automotive	Elon Musk	51.0	United States	Austin	Tesla, SpaceX	Automotive	United States	...	7.5	$21,427,700,000,000	88.2	101.8	78.5	9.6	36.6	328239523.0	37.090240	-95.712891
2	3	114000	Technology	Jeff Bezos	59.0	United States	Medina	Amazon	Technology	United States	...	7.5	$21,427,700,000,000	88.2	101.8	78.5	9.6	36.6	328239523.0	37.090240	-95.712891
3	4	107000	Technology	Larry Ellison	78.0	United States	Lanai	Oracle	Technology	United States	...	7.5	$21,427,700,000,000	88.2	101.8	78.5	9.6	36.6	328239523.0	37.090240	-95.712891
4	5	106000	Finance & Investments	Warren Buffett	92.0	United States	Omaha	Berkshire Hathaway	Finance & Investments	United States	...	7.5	$21,427,700,000,000	88.2	101.8	78.5	9.6	36.6	328239523.0	37.090240	-95.712891

5 rows × 35 columns

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2640 entries, 0 to 2639
Data columns (total 35 columns):
 #   Column                                      Non-Null Count  Dtype  
---  ------                                      --------------  -----  
 0   rank                                        2640 non-null   int64  
 1   finalWorth                                  2640 non-null   int64  
 2   category                                    2640 non-null   object 
 3   personName                                  2640 non-null   object 
 4   age                                         2640 non-null   float64
 5   country                                     2640 non-null   object 
 6   city                                        2568 non-null   object 
 7   source                                      2640 non-null   object 
 8   industries                                  2640 non-null   object 
 9   countryOfCitizenship                        2640 non-null   object 
 10  organization                                325 non-null    object 
 11  selfMade                                    2640 non-null   bool   
 12  status                                      2640 non-null   object 
 13  gender                                      2640 non-null   object 
 14  birthDate                                   2564 non-null   object 
 15  lastName                                    2640 non-null   object 
 16  firstName                                   2637 non-null   object 
 17  title                                       339 non-null    object 
 18  date                                        2640 non-null   object 
 19  state                                       753 non-null    object 
 20  residenceStateRegion                        747 non-null    object 
 21  birthYear                                   2564 non-null   float64
 22  birthMonth                                  2564 non-null   float64
 23  birthDay                                    2564 non-null   float64
 24  cpi_country                                 2456 non-null   float64
 25  cpi_change_country                          2456 non-null   float64
 26  gdp_country                                 2476 non-null   object 
 27  gross_tertiary_education_enrollment         2458 non-null   float64
 28  gross_primary_education_enrollment_country  2459 non-null   float64
 29  life_expectancy_country                     2458 non-null   float64
 30  tax_revenue_country_country                 2457 non-null   float64
 31  total_tax_rate_country                      2458 non-null   float64
 32  population_country                          2476 non-null   float64
 33  latitude_country                            2476 non-null   float64
 34  longitude_country                           2476 non-null   float64
dtypes: bool(1), float64(14), int64(2), object(18)
memory usage: 704.0+ KB

data.describe()

	rank	finalWorth	age	birthYear	birthMonth	birthDay	cpi_country	cpi_change_country	gross_tertiary_education_enrollment	gross_primary_education_enrollment_country	life_expectancy_country	tax_revenue_country_country	total_tax_rate_country	population_country	latitude_country	longitude_country
count	2640.000000	2640.000000	2575.000000	2564.000000	2564.000000	2564.000000	2456.000000	2456.000000	2458.000000	2459.000000	2458.000000	2457.000000	2458.000000	2.476000e+03	2476.000000	2476.000000
mean	1289.159091	4623.787879	65.140194	1957.183307	5.740250	12.099844	127.755204	4.364169	67.225671	102.858520	78.122823	12.546235	43.963344	5.102053e+08	34.903592	12.583156
std	739.693726	9834.240939	13.258098	13.282516	3.710085	9.918876	26.452951	3.623763	21.343426	4.710977	3.730099	5.368625	12.145296	5.542447e+08	17.003497	86.762989
min	1.000000	1000.000000	18.000000	1921.000000	1.000000	1.000000	99.550000	-1.900000	4.000000	84.700000	54.300000	0.100000	9.900000	3.801900e+04	-40.900557	-106.346771
25%	659.000000	1500.000000	56.000000	1948.000000	2.000000	1.000000	117.240000	1.700000	50.600000	100.200000	77.000000	9.600000	36.600000	6.683440e+07	35.861660	-95.712891
50%	1312.000000	2300.000000	65.000000	1957.000000	6.000000	11.000000	117.240000	2.900000	65.600000	101.800000	78.500000	9.600000	41.200000	3.282395e+08	37.090240	10.451526
75%	1905.000000	4200.000000	75.000000	1966.000000	9.000000	21.000000	125.080000	7.500000	88.200000	102.600000	80.900000	12.800000	59.100000	1.366418e+09	40.463667	104.195397
max	2540.000000	211000.000000	101.000000	2004.000000	12.000000	31.000000	288.570000	53.500000	136.600000	142.100000	84.200000	37.200000	106.300000	1.397715e+09	61.924110	174.885971

data.isnull().sum()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Assuming 'df' is your DataFrame
# If not, replace 'df' with your actual DataFrame variable

# Create a heatmap of null values
plt.figure(figsize=(12, 8))
sns.heatmap(data.isnull(), cmap='viridis', cbar=False, yticklabels=False)
plt.title('Visualization of Null Values in the Dataset')
plt.show()

data['age']=data['age'].fillna(data['age'].median())
data['age'].isnull().sum()

0

data['country']=data['country'].fillna('unknown')
data['country'].isnull().sum()

0

data.gdp_country=data.gdp_country.str.replace('$','')
data.gdp_country.head()

C:\Users\Anjal\AppData\Local\Temp\ipykernel_17492\3207136862.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  data.gdp_country=data.gdp_country.str.replace('$','')

0     2,715,518,274,227 
1    21,427,700,000,000 
2    21,427,700,000,000 
3    21,427,700,000,000 
4    21,427,700,000,000 
Name: gdp_country, dtype: object

# Wealth distribution analysis: Explore the distribution of billionaires' wealth across different
# industries, countries, and regions.

top_industry =data['category'].value_counts().head(10)

plt.figure(figsize=(20,6))
sns.barplot(x=top_industry.index, y=top_industry)
plt.title("Top Ten Bilionaires by Industry", fontsize=14) 
plt.xlabel("Industry", fontsize=12) 
plt.ylabel("Amount of Billionaires", fontsize=12) 

Text(0, 0.5, 'Amount of Billionaires')

top_countries=data['country'].value_counts().head(10)
top_countries

United States     754
China             523
India             157
Germany           102
United Kingdom     82
Russia             79
Switzerland        78
Hong Kong          68
Italy              55
Singapore          46
Name: country, dtype: int64

plt.figure(figsize=(15,6))
sns.barplot(x=top_countries.index,y=top_countries)
plt.title("Top Ten Bilionaires by Industry", fontsize=14) 
plt.xlabel("country", fontsize=12) 
plt.ylabel("Amount of Billionaires", fontsize=12) 

Text(0, 0.5, 'Amount of Billionaires')

top_cities=data['city'].value_counts().head(10)
top_cities

New York     99
Beijing      68
Hong Kong    68
Shanghai     64
London       61
Moscow       60
Mumbai       56
Shenzhen     54
Singapore    45
Delhi        37
Name: city, dtype: int64

plt.figure(figsize=(15,6))
sns.barplot(x=top_cities.index,y=top_cities)
plt.title("Top Ten Bilionaires by Region", fontsize=14) 
plt.xlabel("city", fontsize=12) 
plt.ylabel("Amount of Billionaires", fontsize=12) 

Text(0, 0.5, 'Amount of Billionaires')

# Investigate the age, gender, and birthplace demographics of billionaires.

plt.figure(figsize=(20,6))
sns.histplot(data=data,x='age' , bins=30, kde=True)

<AxesSubplot:xlabel='age', ylabel='Count'>

plt.figure(figsize=(20,6))
#sns.histplot(data=data,x='gender',bins=10)
#data["gender"].value_counts().plot(kind="bar", color="blue")

<Figure size 2000x600 with 0 Axes>

<Figure size 2000x600 with 0 Axes>

coc=data['countryOfCitizenship'].value_counts().head(10)
plt.figure(figsize=(20,6))
sns.barplot(x=coc.index,y=coc)

<AxesSubplot:ylabel='countryOfCitizenship'>

# Analyze the proportion of self-made billionaires and those who inherited their wealth.
data.selfMade.head()

0    False
1     True
2     True
3     True
4     True
Name: selfMade, dtype: bool

self_made = data["selfMade"].value_counts()
self_made

True     1812
False     828
Name: selfMade, dtype: int64

# Trends over time: Track changes in billionaire demographics and wealth over the age.]
fw=data['finalWorth'].head(10)
plt.figure(figsize=(20,6))
sns.barplot(data=data,x=fw.index,y=fw,color='red')

<AxesSubplot:ylabel='finalWorth'>

data.finalWorth.head(10)

0    211000
1    180000
2    114000
3    107000
4    106000
5    104000
6     94500
7     93000
8     83400
9     80700
Name: finalWorth, dtype: int64

import plotly.express as px
fig = px.scatter_3d(data, x='age', y='finalWorth', z='category', color='category')
fig.update_layout(title='Final Worth vs. Age and Category')
fig.show()

fig = px.scatter(data, x='age', y='finalWorth', size='finalWorth', color='category')
fig.update_layout(title='Final Worth vs. Age')
fig.show()

# Data Transformation of Top 50 Ranked as World Billionaires
loca_top50 <- data.frame(rank = as.character(df_billion$rank),
                        Name = df_billion$personName,
                        Assets = df_billion$finalWorth,
                        Residence_Country = df_billion$country,
                        lat = df_billion$latitude_country,
                        lng = df_billion$longitude_country) %>% 
            head(50)

# Plot the market into the World Map
map <- leaflet()
map <- addTiles(map = map)
addMarkers(map = map, data = loca_top50, popup = glue("<h5>Rank = {loca_top50$rank}</p>
                                                      Name = {loca_top50$Name}</p>
                                                      Assets = $ {comma(loca_top50$Assets)} B</p>
                                                      Country Residence = {loca_top50$Residence_Country}
                                                      "))

  File "C:\Users\Anjal\AppData\Local\Temp\ipykernel_17492\81726786.py", line 2
    loca_top50 <- data.frame(rank = as.character(df_billion$rank),
                                    ^
SyntaxError: invalid syntax

# Extract year from the 'date' column
data['year'] = data['date'].dt.year

# Group by year and calculate the mean wealth
wealth_distribution_over_time = data.groupby('year')['finalWorth'].mean().reset_index()

# Plotting the trend over time
plt.figure(figsize=(12, 6))
sns.lineplot(x='year', y='finalWorth', data=wealth_distribution_over_time, marker='o')
plt.title('Wealth Distribution Trend Over Time')
plt.xlabel('Year')
plt.ylabel('Average Wealth (in billions)')
plt.grid(True)
plt.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_17492\3249720849.py in <module>
      1 # Extract year from the 'date' column
----> 2 data['year'] = data['date'].dt.year
      3 
      4 # Group by year and calculate the mean wealth
      5 wealth_distribution_over_time = data.groupby('year')['finalWorth'].mean().reset_index()

~\anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
   5900         ):
   5901             return self[name]
-> 5902         return object.__getattribute__(self, name)
   5903 
   5904     def __setattr__(self, name: str, value) -> None:

~\anaconda3\lib\site-packages\pandas\core\accessor.py in __get__(self, obj, cls)
    180             # we're accessing the attribute of the class, i.e., Dataset.geo
    181             return self._accessor
--> 182         accessor_obj = self._accessor(obj)
    183         # Replace the property with the accessor object. Inspired by:
    184         # https://www.pydanny.com/cached-property.html

~\anaconda3\lib\site-packages\pandas\core\indexes\accessors.py in __new__(cls, data)
    510             return PeriodProperties(data, orig)
    511 
--> 512         raise AttributeError("Can only use .dt accessor with datetimelike values")

AttributeError: Can only use .dt accessor with datetimelike values

data['date']

0       4/4/2023 5:01
1       4/4/2023 5:01
2       4/4/2023 5:01
3       4/4/2023 5:01
4       4/4/2023 5:01
            ...      
2635    4/4/2023 5:01
2636    4/4/2023 5:01
2637    4/4/2023 5:01
2638    4/4/2023 5:01
2639    4/4/2023 5:01
Name: date, Length: 2640, dtype: object

!geopandas

Collecting geopandas
  Downloading geopandas-0.14.1-py3-none-any.whl (1.1 MB)
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Requirement already satisfied: pandas>=1.4.0 in c:\users\anjal\anaconda3\lib\site-packages (from geopandas) (1.5.3)
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Collecting fiona>=1.8.21
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Requirement already satisfied: packaging in c:\users\anjal\anaconda3\lib\site-packages (from geopandas) (21.3)
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Collecting click-plugins>=1.0
  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)
Requirement already satisfied: certifi in c:\users\anjal\anaconda3\lib\site-packages (from fiona>=1.8.21->geopandas) (2023.7.22)
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Collecting cligj>=0.5
  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)
Requirement already satisfied: attrs>=19.2.0 in c:\users\anjal\anaconda3\lib\site-packages (from fiona>=1.8.21->geopandas) (21.4.0)
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Installing collected packages: shapely, pyproj, cligj, click-plugins, fiona, geopandas
Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.9.5 geopandas-0.14.1 pyproj-3.6.1 shapely-2.0.2

import geopandas as gpd
from shapely.geometry import Point
import matplotlib.pyplot as plt

# Assuming 'latitude' and 'longitude' columns in your DataFrame
geometry = [Point(xy) for xy in zip(data['longitude_country'], data['latitude_country'])]
geo_df = gpd.GeoDataFrame(data, geometry=geometry)

# World map shapefile (download from https://www.naturalearthdata.com/)
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Plotting the map
fig, ax = plt.subplots(figsize=(15, 10))
world.plot(ax=ax, color='lightgrey')

# Plot billionaire locations on top
geo_df.plot(ax=ax, markersize=30, color='red', alpha=0.7)

plt.title('Distribution of Billionaires Worldwide')
plt.show()

C:\Users\Anjal\AppData\Local\Temp\ipykernel_17492\2621660113.py:10: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Visualizing Sources of Wealth by Region
plt.figure(figsize=(15, 5))
sns.countplot(x='region', hue='selfMade', data=data, palette='viridis')
plt.title('Sources of Wealth for Billionaires by Region')
plt.xlabel('Region')
plt.ylabel('Number of Billionaires')
plt.legend(title='Self-Made', loc='upper right')
plt.show()

# Select the top 10 regions based on the number of billionaires
top_regions = data['country'].value_counts().nlargest(10).index
data_top10 = data[data['country'].isin(top_regions)]

plt.figure(figsize=(15, 5))
sns.boxplot(x='country', y='finalWorth', data=data_top10, palette='viridis')
plt.title('Wealth Distribution of Billionaires in Top 10 Regions')
plt.xlabel('Region')
plt.ylabel('Final Worth (in billions)')
plt.yscale('log')  # Use log scale for better visualization if wealth varies significantly
plt.show()

top_industries = data['industries'].value_counts().nlargest(10).index
data_top_industries = data[data['industries'].isin(top_industries)]

plt.figure(figsize=(15, 7))
sns.countplot(x='industries', hue='gender', data=data_top_industries, palette='viridis')
plt.title('Gender Distribution in Top Industries')
plt.xlabel('Industry')
plt.ylabel('Number of Billionaires')
plt.xticks(rotation=45, ha='right')
plt.legend(title='Gender', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt



# Drop rows with missing values in relevant columns
data = data.dropna(subset=['selfMade', 'age', 'gender', 'industries'])

# Select relevant features and target variable
X = data[['age', 'gender', 'industries']]
y = data['selfMade']

# Convert categorical variables to numerical using one-hot encoding
X = pd.get_dummies(X, columns=['gender', 'industries'], drop_first=True)

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Logistic Regression Model
model = LogisticRegression()
model.fit(X_train, y_train)

# Predictions on the test set
predictions = model.predict(X_test)

# Model Evaluation
accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy:.2f}')

# Classification Report
print('Classification Report:')
print(classification_report(y_test, predictions))

# Confusion Matrix
plt.figure(figsize=(8, 6))
cm = confusion_matrix(y_test, predictions)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False, annot_kws={"size": 16})
plt.title('Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()

Accuracy: 0.74
Classification Report:
              precision    recall  f1-score   support

       False       0.70      0.30      0.42       166
        True       0.75      0.94      0.83       362

    accuracy                           0.74       528
   macro avg       0.73      0.62      0.63       528
weighted avg       0.73      0.74      0.70       528

C:\Users\Anjal\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:814: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

# ROC Curve
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])
roc_auc = auc(fpr, tpr)

plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (AUC = {:.2f})'.format(roc_auc))
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend()
plt.show()

plt.figure(figsize=(10, 6))
sns.histplot(predictions, kde=True, color='skyblue')
plt.title('Distribution of Predicted Probabilities for Being Self-Made')
plt.xlabel('Predicted Probability')
plt.ylabel('Frequency')
plt.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_17492\4272087344.py in <module>
      1 plt.figure(figsize=(10, 6))
----> 2 sns.histplot(predictions, kde=True, color='skyblue')
      3 plt.title('Distribution of Predicted Probabilities for Being Self-Made')
      4 plt.xlabel('Predicted Probability')
      5 plt.ylabel('Frequency')

~\anaconda3\lib\site-packages\seaborn\distributions.py in histplot(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)
   1460     if p.univariate:
   1461 
-> 1462         p.plot_univariate_histogram(
   1463             multiple=multiple,
   1464             element=element,

~\anaconda3\lib\site-packages\seaborn\distributions.py in plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)
    416             kde_kws["cumulative"] = estimate_kws["cumulative"]
    417             log_scale = self._log_scaled(self.data_variable)
--> 418             densities = self._compute_univariate_density(
    419                 self.data_variable,
    420                 common_norm,

~\anaconda3\lib\site-packages\seaborn\distributions.py in _compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale, warn_singular)
    324 
    325             # Estimate the density of observations at this level
--> 326             density, support = estimator(observations, weights=weights)
    327 
    328             if log_scale:

~\anaconda3\lib\site-packages\seaborn\_statistics.py in __call__(self, x1, x2, weights)
    185         """Fit and evaluate on univariate or bivariate data."""
    186         if x2 is None:
--> 187             return self._eval_univariate(x1, weights)
    188         else:
    189             return self._eval_bivariate(x1, x2, weights)

~\anaconda3\lib\site-packages\seaborn\_statistics.py in _eval_univariate(self, x, weights)
    144         support = self.support
    145         if support is None:
--> 146             support = self.define_support(x, cache=False)
    147 
    148         kde = self._fit(x, weights)

~\anaconda3\lib\site-packages\seaborn\_statistics.py in define_support(self, x1, x2, weights, cache)
    117         """Create the evaluation grid for a given data set."""
    118         if x2 is None:
--> 119             support = self._define_support_univariate(x1, weights)
    120         else:
    121             support = self._define_support_bivariate(x1, x2, weights)

~\anaconda3\lib\site-packages\seaborn\_statistics.py in _define_support_univariate(self, x, weights)
     89     def _define_support_univariate(self, x, weights):
     90         """Create a 1D grid of evaluation points."""
---> 91         kde = self._fit(x, weights)
     92         bw = np.sqrt(kde.covariance.squeeze())
     93         grid = self._define_support_grid(

~\anaconda3\lib\site-packages\seaborn\_statistics.py in _fit(self, fit_data, weights)
    135             fit_kws["weights"] = weights
    136 
--> 137         kde = stats.gaussian_kde(fit_data, **fit_kws)
    138         kde.set_bandwidth(kde.factor * self.bw_adjust)
    139 

~\anaconda3\lib\site-packages\scipy\stats\_kde.py in __init__(self, dataset, bw_method, weights)
    205             self._neff = 1/sum(self._weights**2)
    206 
--> 207         self.set_bandwidth(bw_method=bw_method)
    208 
    209     def evaluate(self, points):

~\anaconda3\lib\site-packages\scipy\stats\_kde.py in set_bandwidth(self, bw_method)
    553             raise ValueError(msg)
    554 
--> 555         self._compute_covariance()
    556 
    557     def _compute_covariance(self):

~\anaconda3\lib\site-packages\scipy\stats\_kde.py in _compute_covariance(self)
    565                                                bias=False,
    566                                                aweights=self.weights))
--> 567             self._data_inv_cov = linalg.inv(self._data_covariance)
    568 
    569         self.covariance = self._data_covariance * self.factor**2

~\anaconda3\lib\site-packages\scipy\linalg\_basic.py in inv(a, overwrite_a, check_finite)
    925 
    926     """
--> 927     a1 = _asarray_validated(a, check_finite=check_finite)
    928     if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
    929         raise ValueError('expected square matrix')

~\anaconda3\lib\site-packages\scipy\_lib\_util.py in _asarray_validated(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)
    288     if not objects_ok:
    289         if a.dtype is np.dtype('O'):
--> 290             raise ValueError('object arrays are not supported')
    291     if as_inexact:
    292         if not np.issubdtype(a.dtype, np.inexact):

ValueError: object arrays are not supported

plt.figure(figsize=(8, 6))
sns.countplot(x='selfMade', data=data, palette='viridis')
plt.title('Distribution of Self-Made Status')
plt.xlabel('Self-Made')
plt.ylabel('Count')
plt.show()